Evaluation of the In Vivo Efficacy of the JAK Inhibitor AZD1480 in Uterine Leiomyomas Using a Patient-derived Xenograft Murine Model.

Uterine leiomyomas are common noncancerous hormonally-dependent neoplasms comprised of uterine smooth-muscle cells and fibroblasts. Despite their significant impact on morbidity, effective non-hormonal medical treatments are lacking. In vitro studies have identified the Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling pathway as a promising target in leiomyoma cells.

A transcriptomic comparison of in vitro models of the human placenta.

Introduction: Selecting an in vitro culture model of the human placenta is challenging due to representation of different trophoblast cell types with distinct biological roles and limited comparative studies that define key characteristics of these models. The aim of this research was to compare the transcriptomes of common in vitro models of the human placenta compared to bulk human placental tissue.

Methods: We performed differential gene expression analysis on publicly available transcriptomic data from 7 in vitro models of the human placenta (HTR-8/SVneo, BeWo, JEG-3, JAR, Primary Trophoblasts, Villous Explants, and Trophoblast Stem Cells) and compared to bulk placental tissue from 2 cohort studies (CANDLE and GAPPS) or individual trophoblast cell types derived from bulk placental tissue.

Learning extreme expected shortfall and conditional tail moments with neural networks. Application to cryptocurrency data.

We propose a neural networks method to estimate extreme Expected Shortfall, and even more generally, extreme conditional tail moments as functions of confidence levels, in heavy-tailed settings. The convergence rate of the uniform error between the log-conditional tail moment and its neural network approximation is established leveraging extreme-value theory (in particular the high-order condition on the distribution tails) and using critically two activation functions (eLU and ReLU) for neural networks. The finite sample performance of the neural network estimator is compared to bias-reduced extreme-value competitors using synthetic heavy-tailed data.